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  • Open Access


    Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

    Xiaorui Zhang1,2,3,*, Qijian Xie1, Wei Sun3,4, Yongjun Ren1,2,3, Mithun Mukherjee5

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 47-61, 2023, DOI:10.32604/cmc.2023.042561

    Abstract Fall behavior is closely related to high mortality in the elderly, so fall detection becomes an important and urgent research area. However, the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy. To solve the above problems, this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose. Lightweight OpenPose uses MobileNet as a feature extraction network, and the prediction layer uses bottleneck-asymmetric structure, thus reducing the amount of the network. The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by… More >

  • Open Access


    Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2,3, Miguel Rio1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 19-36, 2023, DOI:10.32604/cmc.2023.032499

    Abstract The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to… More >

  • Open Access


    A Mathematical Model and a Method for the Calculation of the Downhole Pressure in Composite-Perforation Technological Processes

    Xufeng Li1,2,3, Yantao Bi1,2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1699-1709, 2022, DOI:10.32604/fdmp.2022.019741

    Abstract Using the conservation equations for mass, momentum and energy, a model is elaborated to describe the dynamics of high-energy gases in composite-perforation technological processes. The model includes a precise representation of the gunpowder combustion and related killing fluid displacement. Through numerical solution of such equations, the pressure distribution of the high-energy gas in fractures is obtained, and used to determine crack propagation. The accuracy of the model is verified by comparing the simulation results with actual measurements. More > Graphic Abstract

    A Mathematical Model and a Method for the Calculation of the Downhole Pressure in Composite-Perforation Technological Processes

  • Open Access


    Multi-Scale Network with Integrated Attention Unit for Crowd Counting

    Adel Hafeezallah1, Ahlam Al-Dhamari2,3,*, Syed Abd Rahman Abu-Bakar2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3879-3903, 2022, DOI:10.32604/cmc.2022.028289

    Abstract Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering. Moreover, since the crowd images in this case can range from low density to high density, detection-based approaches are hard to apply for crowd counting. Recently, deep learning-based regression has become the prominent approach for crowd counting problems, where a density-map is estimated, and its integral is further computed to acquire the final count result. In this paper, we put forward a novel multi-scale network (named 2U-Net)… More >

  • Open Access


    Skeleton Keypoints Extraction Method Combined with Object Detection

    Jiabao Shi1, Zhao Qiu1,*, Tao Chen1, Jiale Lin1, Hancheng Huang2, Yunlong He3, Yu Yang3

    Journal of New Media, Vol.4, No.2, pp. 97-106, 2022, DOI:10.32604/jnm.2022.027176

    Abstract Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point extraction method combined with object… More >

  • Open Access


    Why Insisting in Being Volunteers? A Practical Case Study Exploring from Both Rational and Emotional Perspectives

    Kuei-Feng Chang1, Wen-Goang Yang2, Ya-Wen Cheng3, I-Tung Shih2,*

    International Journal of Mental Health Promotion, Vol.24, No.2, pp. 219-236, 2022, DOI:10.32604/ijmhp.2022.018187

    Abstract This study explored the mechanism on how volunteers as rationalists use rationalism during their cognitive appraisal process when dealing with emotional events in their social helping behavior such as international rescue events. The authors used the triangulation method to include three studies (Study 1 is a qualitative research which explored ways of TCF leader’s inspiring their volunteer workers; Study 2 is a quantitative research on the decision-making process of volunteer individuals involving in international rescue activities; Study 3 is a quantitative research on individuals’ motivation for joining social helping behavior) for validation of Tzu Chi Foundation (TCF), which is a… More >

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